Startup’s Data Helps Women Succeed With In Vitro Fertilization

In vitro fertilization (IVF), a last recourse for women who want to get pregnant, is expensive, and its outcome is uncertain. Now a Silicon Valley data-mining startup is significantly improving predictions about whether a woman’s IVF will succeed.

Reproductive health scientist Dr. Mylene Yao and Stanford statistics professor Wing Wong, founders of Univfy, compare detailed personal health information with large data sets taken from past efforts with thousands of women to predict the likely results of IVF treatment. It’s easy to see why it might be in demand.

IVF, an assisted reproduction method that fertilizes an egg in a lab dish before transferring the embryo to the mother, is far more likely to fail than to succeed according to data from the Society for Assisted Reproductive Technology. The price tag either way is upwards of $11,000, including requisite medications. Costs are even higher for those who freeze embryos for follow-up attempts. Most insurance plans do not cover IVF.

Mylene Yao, co-founder and CEO of Univfy

Typically, physicians predict a woman’s chance of having a child through IVF based on nothing more than her chronological age—the older she is, the less likely the procedure is to succeed. Research presented last year at the Annual Meeting of the American Society for Reproductive Medicine, for instance, showed that “a patient over 40 had a 13 percent chance of live birth after one fresh and one frozen-thawed IVF cycle.”

Those are daunting odds for women facing huge expense for an emotionally-fraught last resort for getting pregnant. But Yao, who formerly ran a Stanford lab that studied embryonic development, argues that chronological age, though assumed to predict ovarian function and egg viability, is not the only—or even the best—factor to consider in such high-stakes decisions. “Women’s ovaries are aging at different paces, so chronological age isn’t as useful” as other patient-specific information, Yao says. And, she adds, diminished ovarian function is not the only contributor to infertility.

Yao points out that in the same way that it can often take several months of trying before a fertile couple can conceive naturally, “not everybody is going to have success with their first IVF treatment.” The problem, Yao says, is that when their first attempt fails, most women don’t know whether it was just bad luck or if underlying medical reasons hurt their chances.

Stanford statistic professor Wing Wong, co-founder of Univfy

So she and Wong developed an algorithm that considers inputs including a woman’s body mass index, endometrial thickness, smoking history, past pregnancies, and reproductive health history, as well as her partner’s sperm count. These factors, contrasted with data from tens of thousands of other women, can be a much better predictor than age of any individual woman’s chances of having a live birth with an IVF treatment. Says Yao, “This isn’t about big data, it’s about rich data.”

Yao and Wong call the approach “deep phenotype profiling,” and their angel-investor-backed company delivers pre-IVF test results instantly online for $250. Another test, priced at $350, factors in the results of an initial IVF attempt, including egg count and embryo quality, to predict the outcome of a second attempt.

So far, the Univfy tests do not consider genetic data. But Yao says that as biomarkers for infertility become available, those could be incorporated. (The test is only available to women under age 43, or up to age 45 for those who have data from an initial IVF cycle.)

The chances of pregnancy via IVF may be considerably higher than generally believed for many women. A recent study that applied the model to data from leading IVF clinics in Boston, Ottawa, and Valencia, Spain, showed the test could have predictive power and encourage more people to attempt IVF. “Consistently, we have found that over half of patients—in some cases 60-80 percent—have much higher chances of success when this personalized approach is used, compared to conventional age-based estimates,” Yao says. “A vast majority of patients are getting an underestimation of their chances.”

There are many other potential applications of the test. Even women who aren’t struggling with infertility could use it to make family-planning decisions. Yao says her team has also developed a test to predict the risks of multiple births if two or more embryos are transferred in an IVF cycle.

A publicity campaign employing Facebook, Pinterest, YouTube, Twitter, along with a partnership with the national infertility association Resolve is getting under way. Yao is confident in the market for her product: “We’ve done surveys with patients in different regions of the country. It seemed unanimous that they want this information and they tell us that ‘it’s a no-brainer’ to pay for it.”

This sounds great but why paying for such an application? Don’t ivf couples pay enough for their treatments? Additionally, I believe that the purpose of academic research – especially on health issues – should be to provide new insights available for all not just for those they can afford it…

Adrienne Burke

Thanks for the comment, Ck. Yes, as the article points out, IVF couples do pay a lot for their treatments. The point of these tests is to help them avoid spending thousands of dollars unnecessarily — or at least, for a few hundred dollars, to get more datapoints to predict their chances of success before they invest so much money in the procedure or a followup procedure.

http://twitter.com/adajane Adrienne Jane Burke

Thanks for the comment, Ck. Yes, as the article points out, IVF couples do pay a lot for their treatments. The point of these tests is to help them avoid spending thousands of dollars unnecessarily — or at least, for a few hundred dollars, to get more datapoints to predict their chances of success before they invest so much money in the procedure or a followup procedure.